Project
Random Forest from Scratch
Hard
54 completions
~ 19 hours
4.2Consolidate your knowledge of the Random forest algorithm. Compare it against a standalone decision tree and check if your algorithm overfits when you increase the number of trees in it.
Provided by
JetBrains Academy
About
In this project, we will dive into implementing one of the most popular while simple enough ensemble algorithms — Random forest. Implement the entire algorithm from scratch using numpy. Test it on the titanic dataset from sklearn.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Use the default decision tree from sklearn to get some initial quality values.
Implement the bootstrap algorithm for your Random forest.
Implement the .fit method to make it possible for your Random forest to train.
Implement the .predict method to make for your Random forest.
Use your Random forest to get predictions and compare them with the results from Stage 1.
Check whether your Random forest for overfitting when you increase the trees.
Reviews
7 months ago
Basic concepts of numpy and machine learning. Implemented Random Forest and learned how it works.
8 months ago
if you are still not familar with random foresting this is your last chance. you will not only understand you will master it.
12 months ago
I learned how to make my own classification model, compare it to other models, and how to find the general relationship between a model's parameters and the accuracy of its predictions.
4.2
Learners who completed this project within the Introduction to Data Science course rated it as follows: